This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.

The dorg, the latest batch of digital organisms, will one day be placed in a little world to work out their destiny. The notion is to try and coax them into becoming intelligent. There's a bunch of coding that Brad has to finish first. In the meantime, they've been tuned and tested with a genetic algorithm. Today, we talk about genetic algorithms and how they can be used to speed up evolution, and point the dorg in what will hopefully turn out to be the right direction.

The area of computation called artificial intelligence (AI) is falsified by describing a previous 1972 falsification of AI by British applied mathematician James Lighthill. It is explained how Lighthill's arguments continue to apply to current AI. It is argued that AI should use the Popperian scientific method in which it is the duty of every scientist to attempt to falsify theories and if theories are falsified to replace or modify them. The paper describes the Popperian method in detail and discusses Paul Nurse's application of the method to cell biology that also involves questions of mechanism and behavior. Arguments used by Lighthill in his original 1972 report that falsified AI are discussed. The Lighthill arguments are then shown to apply to current AI. The argument uses recent scholarship to explain Lighthill's assumptions and to show how the arguments based on those assumptions continue to falsify modern AI. An important focus of the argument involves Hilbert's philosophical programme that defined knowledge and truth as provable formal sentences. Current AI takes the Hilbert programme as dogma beyond criticism while Lighthill as a mid 20th century applied mathematician had abandoned it. The paper uses recent scholarship to explain John von Neumann's criticism of AI that I claim was assumed by Lighthill. The paper discusses computer chess programs to show Lighthill's combinatorial explosion still applies to AI but not humans. An argument showing that Turing Machines (TM) are not the correct description of computation is given. The paper concludes by advocating studying computation as Peter Naur's Dataology.

Irrespective of your ideological viewpoint, the collective Women's marches around the world this past weekend were inspiring and illustrative of the collective power of women. Simultaneously, regions of the United States were being ravaged by deadly tornadic storms. Modern computer models were critical in identifying the evolution of atmospheric conditions to support severe weather. There is a curious connection between these two trains of thought. Trust me, I am about to make the connection.

Concerns have recently been widely expressed that artificial intelligence presents a threat to humanity. For instance, Stephen Hawking is quoted in Cellan-Jones1 as saying: "The development of full artificial intelligence could spell the end of the human race." Similar concerns have also been expressed by Elon Musk, Steve Wozniak, and others. Such concerns have a long history. John von Neumann is quoted by Stanislaw Ulam8 as the first to use the term the singularitya--the point at which artificial intelligence exceeds human intelligence.

Unlike conventional learning theories and tenets, our doubts are "Do we really need so many different types of learning algorithms (SVM, BP, etc) for so many different types of networks (different types of SLFNs (RBF networks, polynomial networks, complex networks, Fourier series, wavelet networks, etc) and multi-layer of architecfures, different types of neurons, etc)? Is there a general learning scheme for wide type of different networks (SLFNs and multi-layer networks)? Neural networks (NN) and support vector machines (SVM) play key roles in machine learning and data analysis. Feedforward neural networks and support vector machines are usually considered different learning techniques in computational intelligence community. Both popular learning techniques face some challenging issues such as: intensive human intervene, slow learning speed, poor learning scalability. It is clear that the learning speed of feedforward neural networks including deep learning is in general far slower ...